Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game

TL;DR

This paper presents a hierarchical stochastic scheduling approach for multi-community energy systems under uncertainty, integrating renewable scenario generation, demand response, and a Stackelberg game framework to optimize profits and costs.

Contribution

It introduces a novel hierarchical stochastic scheduling method using Wasserstein GANs and Stackelberg game theory for multi-community energy systems under uncertainty.

Findings

Effective renewable scenario generation with Wasserstein GANs.

Hierarchical scheduling improves profit and cost efficiency.

Distributed solution method converges in practical examples.

Abstract

An operating entity utilizing community-integrated energy systems with a large number of small-scale distributed energy sources can easily trade with existing distribution markets. To solve the energy management and pricing problem of multi-community integrated energy systems (MCIESs) with multi-energy interaction, this study investigated a hierarchical stochastic optimal scheduling method for uncertain environments. To handle multiple uncertainties, a Wasserstein generative adversarial network with a gradient penalty was used to generate renewable scenarios, and the Kmeans++ clustering algorithm was employed to generate typical scenarios. A Stackelberg-based hierarchical stochastic schedule with an integrated demand response was constructed, where the MCIES operator acted as the leader pursuing the maximum net profit by setting energy prices, while the building users were followers who adjusted their energy consumption plans to minimize their total costs. Finally, a distributed iterative solution method based on a metaheuristic was designed. The effectiveness of the proposed method was verified using practical examples.